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At present, the integration of renewables, and especially wind power plants, in the electricity market, accentuates the role of the capacity market mechanism as an important means for the further diffusion of mature renewable energy technologies. The Capacity market is one of the main solutions for improving supply adequacy. This paper studies the impact of the capacity market on supply adequacy in an advanced long-term electricity market model with a noticeable share of renewable energy, simulating the market by applying a bottom-up approach. In this regard, the entire electricity market of Europe is simulated in the HECTOR model, taking a system dynamics perspective. The model simulates the electricity market of 28 European countries, with a special focus on Germany. In addition, the auctions studied are analyzed on two different spatial levels, the state and the national level. In the state level design, all wind power plants within Germany are assumed to bid within the federal state they are located and the market is cleared at the state level. In the national level design , all wind power plants located in different states bid in a national market, which represents the German market. Comparing the results obtained for the market design reveals the superior spatial platform for the implementation of renewable energy auctions. The results obtained allow identification of the influence of capacity market on the performance of the electricity market and wind generation investment considering detailed and comprehensive categorization of wind power plants. Categorization of wind technologies is based on their regional potential. Furthermore, impacts of capacity market on market performance of neighboring regions in an interconnected market, considering different spatial platforms will be scrutinized. Methods: HECTOR is a simulation model which simulates supply side of electricity market of Europe with system dynamics perspective. It consists of five main modules which all of them are executed simultaneously with hourly resolution. Main modules includes: capacity investment, plant bidding logic, decentral production, residual demand and market clearance. The objective function of this long-term model, which minimizes total system costs subject to a set of constraints, is being solved in market clearance module. This function matches supply and residual demand considering regional constraints such as import and export costs and transmission capacities in order to find lowest total system cost. A zooming approach has been used to politically measure the optimal regional resolution in Germany which market performance can be maximized with. Results: As the database of this study is not completed yet, we just referred to the expected results from our work in progress which will be as follows: First, a group of results, provides us information about the impacts of capacity market on the installed capacity of wind technologies in different regions of Germany

In this paper, we review the state-of-the-art and common practice of energy and climate modeling vis-à-vis the rebound literature, in particular regarding how macroeconomic energy and climate models quantify and include energy and greenhouse gas rebound effects. First, we focus on rebound effects in models of costless energy efficiency improvement that hold other attributes constant (zero-cost breakthrough), and an energy efficiency policy that may be bundled with other product changes that affect energy use (policy-induced efficiency improvement) (Gillingham et al. 2015). Second, we examine macroeconomic studies focusing on energy efficiency both in industry and in private households. Third, we go through a general theoretical revision from micro- to macroeconomic levels (the aggregation level) to include a review of the so-called meso-level studies (focused on the analysis of the production side). From 118 recent studies along the aggregation level, out of which 25 compute rebound calculations, we find that the average energy rebound effect is 58% with a standard deviation of 58%, and when we include green house gas rebound calculations, the magnitude is of the order of 43% with a standard deviation of 55%. Finally, we argue that the rebound effect is a phenomenon that requires a sound understanding of the complex interactions from different dimensions (e.g. aggregation level, heterogeneity, climate, energy conservation and economic growth), and we provide some ideas and motivations for future research.